phangorn (version 2.12.1)

discrete.gamma: Discrete Gamma and Beta distribution

Description

discrete.gamma internally used for the likelihood computations in pml or optim.pml. It is useful to understand how it works for simulation studies or in cases where .

Usage

discrete.gamma(alpha, k)
discrete.beta(shape1, shape2, k)
plot_gamma_plus_inv(shape = 1, inv = 0, k = 4, discrete = TRUE,
  cdf = TRUE, append = FALSE, xlab = "x", ylab = ifelse(cdf, "F(x)",
  "f(x)"), xlim = NULL, verticals = FALSE, edge.length = NULL,
  site.rate = "gamma", ...)
plotRates(obj, cdf.color = "blue", main = "cdf", ...)

Value

discrete.gamma returns a matrix.

Arguments

alpha

Shape parameter of the gamma distribution.

k

Number of intervals of the discrete gamma distribution.

shape1, shape2

non-negative parameters of the Beta distribution.

shape

Shape parameter of the gamma distribution.

inv

Proportion of invariable sites.

discrete

logical whether to plot discrete (default) or continuous pdf or cdf.

cdf

logical whether to plot the cumulative distribution function or density / probability function.

append

logical; if TRUE only add to an existing plot.

xlab

a label for the x axis, defaults to a description of x.

ylab

a label for the y axis, defaults to a description of y.

xlim

the x limits of the plot.

verticals

logical; if TRUE, draw vertical lines at steps.

edge.length

Total edge length (sum of all edges in a tree).

site.rate

Indicates what type of gamma distribution to use. Options are "gamma" (Yang 1994) and "gamma_quadrature" using Laguerre quadrature approach of Felsenstein (2001)

...

Further arguments passed to or from other methods.

obj

an object of class pml

cdf.color

color of the cdf.

main

a main title for the plot.

Author

Klaus Schliep

Details

These functions are exported to be used in different packages so far only in the package coalescentMCMC, but are not intended for end user. Most of the functions call C code and are far less forgiving if the import is not what they expect than pml.

See Also

pml.fit, stepfun, pgamma, pbeta,

Examples

discrete.gamma(1, 4)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(2,1))
plot_gamma_plus_inv(shape=2, discrete = FALSE, cdf=FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE, cdf=FALSE)

plot_gamma_plus_inv(shape=2, discrete = FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE)
par(old.par)